CNN classifiers work by holographic superposition and destructive interference in pixel space rather than selecting cleaned features, as proven by a new adjoint inversion framework that also yields a covariance-volume channel selection algorithm.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DAP improves ViT attribution maps by injecting decision-relevant gradients into attention propagation, producing more class-sensitive and faithful explanations than standard attention rollout.
citing papers explorer
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Adjoint Inversion Reveals Holographic Superposition and Destructive Interference in CNN Classifiers
CNN classifiers work by holographic superposition and destructive interference in pixel space rather than selecting cleaned features, as proven by a new adjoint inversion framework that also yields a covariance-volume channel selection algorithm.
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Decision-Aware Attention Propagation for Vision Transformer Explainability
DAP improves ViT attribution maps by injecting decision-relevant gradients into attention propagation, producing more class-sensitive and faithful explanations than standard attention rollout.